Understanding Regulatory Changes: Deep Learning in Sustainable Finance and Banking

Bogdan Ionut Anghel () and Radu Lupu
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Bogdan Ionut Anghel: Department of International Business and Economics, Bucharest University of Economic Studies, 010404 Bucharest, Romania
Radu Lupu: Institute for Economic Forecasting, Romanian Academy, 050711 Bucharest, Romania

JRFM, 2024, vol. 17, issue 7, 1-13

Abstract: This paper examines the regulatory impact on the European Banking Sector using advanced deep learning techniques to analyze the relationship between Sustainable Finance guidelines and the SX7P Index from January 2012 to December 2023. Utilizing Long Short-Term Memory Auto-encoder (LSTM-AE), Variational Autoencoder (VAE), and Convolutional Neural Network (CNN) for anomaly detection, the study compares anomalies and investigates their correlation with European Banking Authority (EBA) events and Sustainable Finance guidelines from January 2020 to December 2023. Through the analysis of 43 pertinent EBA documents, the research identifies patterns and variations in anomalies, assessing their association with regulatory changes. The results reveal significant anomalies aligning with regulatory events, indicating a potential causal relationship. Notably, the VAE methodology shows the strongest correlation between EBA Sustainable Finance events and anomalies. This research advances the understanding of deep learning applications in financial markets and offers valuable insights for policymakers and financial institutions regarding regulatory shifts in Sustainable Finance.

Keywords: deep learning; anomaly detection; sustainable finance; banking; regulation (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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